04367nam 2201177z- 450 991055764580332120231214133627.0(CKB)5400000000044995(oapen)https://directory.doabooks.org/handle/20.500.12854/76890(EXLCZ)99540000000004499520202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierEnergy Data Analytics for Smart Meter DataBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 electronic resource (346 p.)3-0365-2016-3 3-0365-2017-1 The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal.Technology: general issuesbicsscsmart gridnontechnical losseselectricity theft detectionsynthetic minority oversampling techniqueK-means clusterrandom forestsmart gridssmart energy systemsmart meterGDPRdata privacyethicsmulti-label learningNon-intrusive Load Monitoringappliance recognitionfryze power theoryV-I trajectoryConvolutional Neural Networkdistance similarity matrixactivation currentelectric vehiclesynthetic dataexponential distributionPoisson distributionGaussian mixture modelsmathematical modelingmachine learningsimulationNon-Intrusive Load Monitoring (NILM)NILM datasetspower signatureelectric load simulationdata-driven approachessmart meterstext convolutional neural networks (TextCNN)time-series classificationdata annotationnon-intrusive load monitoringsemi-automatic labelingappliance load signaturesambient influencesdevice classification accuracyNILMsignatureload disaggregationtransientspulse generatorsmart meteringsmart power gridspower consumption dataenergy data processinguser-centric applications of energy dataconvolutional neural networkenergy consumptionenergy data analyticsenergy disaggregationreal-timesmart meter datatransient load signatureattention mechanismdeep neural networkelectrical energyload schedulingsatisfactionShapley Valuesolar photovoltaicsreviewdeep learningdeep neural networksTechnology: general issuesReinhardt Andreasedt1295460Pereira LucasedtReinhardt AndreasothPereira LucasothBOOK9910557645803321Energy Data Analytics for Smart Meter Data3023469UNINA